Benralizumab for acute thromboembolism in hypereosinophilic syndrome: a case report
Bibliographic record
Abstract
BACKGROUND: Hypereosinophilic syndrome is a group of disorders characterized by organ dysfunction caused by hypereosinophilia, which frequently leads to thromboembolic complications with potentially fatal outcomes. Interleukin-5, a key cytokine that promotes the differentiation and activation of eosinophils, has been identified as a therapeutic target. Anti-interleukin-5 antibody therapy has demonstrated efficacy in reducing eosinophil counts and enabling steroid dose tapering in patients with hypereosinophilic syndrome. This report describes the case of a patient with severe thromboembolism associated with hypereosinophilic syndrome during the acute phase who was successfully treated with benralizumab, an anti-interleukin-5 receptor alpha antibody. CASE PRESENTATION: A 22-year-old woman presented with a persistent cough and was diagnosed with eosinophilic pneumonia and portal vein thrombosis. Although eosinophilic pneumonia improved with corticosteroid therapy, thrombotic complications worsened despite additional anticoagulant treatment. The administration of benralizumab led to marked improvement in thrombosis, resulting in clinical recovery. CONCLUSIONS: This case suggests that the early administration of anti-interleukin-5 receptor antibody therapy may be a valuable treatment option for refractory thrombosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".